class_docstr: RCNN bbox head
  
  Args:
      head (object): the head module instance, e.g., `ResNetC5`, `TwoFCHead`
      box_coder (object): `BoxCoder` instance
      nms (object): `MultiClassNMS` instance
      num_classes: number of output classes
docstr: Get bbox head output.
  
  Args:
      roi_feat (Variable): RoI feature from RoIExtractor.
  
  Returns:
      cls_score(Variable): Output of rpn head with shape of
          [N, num_anchors, H, W].
      bbox_pred(Variable): Output of rpn head with shape of
          [N, num_anchors * 4, H, W].
docstr_tok: ["Get","bbox","head","output",".","Args",":","roi_feat","(","Variable",")",":","RoI","feature","from","RoIExtractor",".","Returns",":","cls_score","(","Variable",")",":","Output","of","rpn","head","with","shape","of","[","N",",","num_anchors",",","H",",","W","]",".","bbox_pred","(","Variable",")",":","Output","of","rpn","head","with","shape","of","[","N",",","num_anchors","*","4",",","H",",","W","]","."]
code: def _get_output(self, roi_feat):
      """
      Get bbox head output.
  
      Args:
          roi_feat (Variable): RoI feature from RoIExtractor.
  
      Returns:
          cls_score(Variable): Output of rpn head with shape of
              [N, num_anchors, H, W].
          bbox_pred(Variable): Output of rpn head with shape of
              [N, num_anchors * 4, H, W].
      """
      head_feat = self.get_head_feat(roi_feat)
      # when ResNetC5 output a single feature map
      if not isinstance(self.head, TwoFCHead):
          head_feat = fluid.layers.pool2d(
              head_feat, pool_type="avg", global_pooling=True
          )
      cls_score = fluid.layers.fc(
          input=head_feat,
          size=self.num_classes,
          act=None,
          name="cls_score",
          param_attr=ParamAttr(
              name="cls_score_w",
              initializer=Normal(loc=0.0, scale=0.01),
          ),
          bias_attr=ParamAttr(
              name="cls_score_b",
              learning_rate=2.0,
              regularizer=L2Decay(0.0),
          ),
      )
      bbox_pred = fluid.layers.fc(
          input=head_feat,
          size=4 * self.num_classes,
          act=None,
          name="bbox_pred",
          param_attr=ParamAttr(
              name="bbox_pred_w",
              initializer=Normal(loc=0.0, scale=0.001),
          ),
          bias_attr=ParamAttr(
              name="bbox_pred_b",
              learning_rate=2.0,
              regularizer=L2Decay(0.0),
          ),
      )
      return cls_score, bbox_pred
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from collections import OrderedDict
  from paddle import fluid
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Normal, Xavier
  from paddle.fluid.regularizer import L2Decay
  from paddle.fluid.initializer import MSRA
label_desc: Use image node_id 3 for calling the BBoxHead obj's underlying member method code with example usage: obj._get_output(roi_feat) and returns: cls_score,  bbox_pred